As used herein, a medium refers to a banknote, a check, a thicket, a certificate, etc., the thickness of which is substantially smaller than the width or length.
Medium recognition apparatuses are used for various types of automated financial devices and medium handling devices, such as banknote recognition devices, vending machines, and coin exchangers, to recognize the magnetic, images, embedded images, fluorescent ink, numbers, and characters, which are printed on media, and determine the type of the media, whether they have been counterfeited or not, etc. The medium recognition apparatuses determine the type of media based on characteristic patterns peculiar to respective medium types.
FIG. 1 shows a series of steps of a method for recognizing the type of banknotes by using a banknote type recognition apparatus according to the prior art.
Referring to FIG. 1, in the first step (S1), the banknote recognition apparatus scans an image of a banknote by using an image sensor. The scanned image is generally larger than the banknote size to avoid scanning only a part of the banknote image due to vibration that may occur during the transfer process. Therefore, the scanned image includes both a banknote image and a marginal image around it.
In the second step (S2), the banknote recognition apparatus determines whether or not the banknote has been aligned. If the banknote is skewed, the skew is corrected. In the third step (S3), the banknote recognition apparatus extracts the banknote image by excluding the marginal image.
The banknote recognition apparatus then extracts the boundary lines of the banknote image (S4), and extracts the characteristic pattern of the banknote image (S5). As used herein, the characteristic pattern refers to the direction of boundary lines, end points, branch points, line values, etc. of a number of regions, into which the banknote image has been divided. The banknote recognition apparatus compares the extracted characteristic pattern with data regarding respective banknote types stored in the database (S6), and determines the type of the banknote (S7).
In order to extract the characteristic pattern, however, the banknote recognition apparatus must conduct complicated processes. Particularly, the apparatus scans an image, converts it into a filtered black/white image, and conducts additional digital filtering to create binary codes corresponding to the digital image. The binary codes are operated to identify the edge lines of the banknote. Then, the apparatus conducts quantization, conversion of the binary codes into vector tablets, coordinate rendering, etc. with regard to separate regions.
As such, the conventional banknote recognition apparatus relying on the characteristic pattern has a problem in that the algorithm for extracting characteristic patterns necessary to determine the banknote type is complicated, and the large number of processing steps slow down the operation.
In addition, use of the characteristic pattern of the image is vulnerable to vibration occurring while the banknote is transferred, noise of circuit devices, change in output of LEDs for illuminating the banknote, and variation in sensitivity of the image sensor.
The characteristic pattern of each banknote type must be stored in a database, which requires a large memory capacity.
If a color pattern scheme is adopted, the type of an inserted banknote is determined by emitting light to the front and rear surfaces of the banknote and identifying the shape of the banknote based on sensing data regarding the reflected or transmitted light. This scheme has a problem in that the banknote type can hardly be determined based on insufficient information, and it takes a long time to obtain the necessary data.
Schemes relying on fluorescent waves and UV rays have a problem in that the process of detecting fluorescent waves emitted from fluorescent substances is complicated and prolongs the operation.
Schemes employing size sensors to recognize banknotes have the problem of poor accuracy of determining the banknote type.